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Why it matters: Introduced the Transformer architecture, replacing recurrence with self-attention for parallelizable sequence modeling.

Significance: Became the foundation for state-of-the-art models like GPT, BERT, and countless others, revolutionizing natural language processing and beyond.

Why it matters: Demonstrated that scaling up models and data could lead to emergent capabilities, fundamentally shifting the paradigm from task-specific models to foundation models.

Significance: Cemented the notion of scale and data as key drivers for AI progress.

Why it matters: Showed the power of deep convolutional networks (CNNs) on large datasets, sparking the deep learning revolution.

Significance: Without AlexNet, we might not have had the momentum to pursue Transformers in the first place.

Why it matters: Bridged the gap between deep learning and reinforcement learning (RL), showing how neural networks could learn complex tasks directly from pixel input.

Significance: Paved the way for advanced RL models like AlphaGo and MuZero.

Why it matters: Introduced the seq2seq paradigm, enabling models to generate entire sequences of text rather than individual predictions.

Significance: Provided the foundation for machine translation and, eventually, Transformers.

Why it matters: AlphaGo showcased the power of combining neural networks with search algorithms to achieve superhuman performance on a complex task.

Significance: Demonstrated AI's potential for strategic reasoning and set the stage for AlphaZero and MuZero.

Why it matters: Proposed that smaller 'winning subnetworks' exist within large, over-parameterized neural networks, challenging assumptions about scale and training efficiency.

Significance: Inspired a wave of research on model sparsity and efficient AI.

Why it matters: Introduced the Adam optimizer, which is now the default choice for training neural networks.

Significance: Training Transformers and large neural networks would have been vastly more difficult without it.